Hiring Data Engineer with Ontology and Knowledge Graph Skills for Hybrid Role in Financial Sector
Intuitive is looking for an experienced data engineer with ontology and knowledge graph experience. This will be a hybrid role in financial sector with approx 2 days a week in office in Malvern, PA. Public link and application info here linkedin.com/jobs/view/4436857682?refId=…&trackingId=… Or contact me with more technical questions: peter.rivett@intuitive.ai Must Have These are the capabilities we cannot compromise on. They reflect information discipline and engineering maturity rather than tool familiarity. Data Engineering and Information Management Fundamentals • Strong data engineering background with a clear understanding of how data is structured, governed, versioned, and moved across systems. Experience designing durable information models that outlive any single source or implementation. Required experience includes AWS data platforms, specifically S3‑based data lakes and AWS‑managed databases. Familiarity with treating data as a long‑lived information asset is essential. Information Modeling
Ability to organize business concepts clearly, separate meaning from storage, and map real data to conceptual models. Comfort aligning internal models to shared or external standards rather than optimizing only for local schemas.
Abstract Thinking and Adaptability
Comfort working in ambiguity and reasoning from first principles. Ability to learn new modeling approaches, technologies, and standards quickly, adjust assumptions, and refine models as understanding deepens.
Open Standards Orientation
Experience working with open standards in any technology domain, including data formats, APIs, identifiers, or metadata specifications. This may include REST or GraphQL APIs, schema standards, or industry data models. Demonstrated ability to read standards, understand intent, and apply them pragmatically even when the standard is new.
Engineering Mindset
Practical experience integrating conceptual models into real systems. This includes mapping models to data layers, exposing or consuming APIs such as GraphQL, supporting mock or lightweight integrations, and using version control and basic DevOps practices with discipline.
Communication
Ability to explain complex information and data concepts in plain language and connect technical decisions to business outcomes. Clear written and verbal communication is essential.
Nice to Have These skills accelerate impact but can be learned by the right engineer. Ontology and Knowledge Graph Technologies Familiarity with ontology and semantic standards such as SKOS, RDF, OWL, and SHACL, or hands‑on experience with knowledge graph technologies and graph databases. Prior depth is helpful but not required if the engineer demonstrates strong information modeling instincts and learning ability. Asset Management Domain Knowledge Understanding of investment products, asset management concepts, and common industry schemas. Domain exposure helps, but strong modeling and engineering skills can bridge gaps. Change Management Awareness Sensitivity to how new standards, APIs, and information structures are adopted within organizations. Appreciation for governance, ownership, and the realities of evolving legacy practices. _____________________________________
